In house developed software was used for all tests and is provided as supplementary material. The code was written in Python, making use of the libraries Numpy [1]} and Scipy [2]} for vectorisation, random number generation and linear algebra operations. Monte Carlo simulations were performed by sampling a number of trajectories built to obey the transition rules of the Markov chain; this was done by generating a random number \(r \in [0, 1)\)  at each step, and choosing the transition \(i \rightarrow j\)  for which
\(\sum _n^{j-1} P_{in} \le r < \sum _n^{j} P_{in}\) 
